Statistical and Machine Learning Approaches for Energy Consumption Forecasting Using Time Series Analysis

Energy production is a rapidly growing activity, especially with the impacts of climate change. It has even become a competitive activity among countries. However, this production is not constant or continuous most of the time, as it depends on external factors such as weather conditions or, in some cases, fossil fuel production. Therefore, predicting energy production has become essential to optimize and manage its efficiency. In this study, a time series of renewable energy production is predicted using statistical models such as ARIMA and SARIMAX, as well as machine learning models such as LSTM and Gaussian Process Regression (GPR). These models are compared, based on evaluation metrics, on predictions made by each model, and on the forecasting over a period of 72 steps. After applying the various comparison techniques, the best-performing model is SARIMAX, with an MSE of 0.000031, an RMSE of 0.0026, an MAE of 0.0015 , and an R² of 99.98%. Furthermore, this model predicts the data as effectively as other models and provides near-perfect forecasting.